The extracted Lc depth-profiles can be used to determine the minority carrier diffusion length and back-surface recombination velocity in CuSe2 (CIGS) thin film solar cells . During the measurement of iLP, the CIGS thin film is generally exposed to air. The CIGS thin films can be degraded by air exposure . Therefore, it will be helpful to know the effect of air exposure time of CIGS thin films on the iLP values to properly estimate the electrical quality of CIGS thin films.Wavelength-dependent (i.e. penetration-depth-dependent) lateral photocurrent ( Raw data related to Figs. 2–4 are available within this article as a supplementary file.Lateral photocurrent  of narrow bandwidth light, generated by passing white-light through the optical bandpass filters, are also displayed in In order to measure wavelength-dependent  pattern , we plac1.1.2iLP values as a function of air exposure time of the CIGS film. The first set of iLP measurement was done within 5 mins after CdS wet-etching. We set these values a reference to investigate the effect of air exposure time of the CIGS thin film on the wavelength-dependent iLP values. After additional air exposure for 10 mins , the iLP values were dropped by less than 1%. With further increasing air exposure time, the iLP values were steadily decreased. Upon additional air exposure for approximately 45 mins , the iLP values were dropped by 5–12%.1.2oc) of 0.673 V, a short-circuit current density (Jsc) of 34.2 mA/cm2, and a fill factor (FF) of 74.9%.The electrical quality of the employed CIGS layer can be evaluated by characterizing a real CIGS solar cell, where the whole surface of the CIGS thin film is covered by CdS. The current density-voltage curve of the fabricated CIGS solar cell, whose schematic is shown in 2This section is divided by two parts. The first one is the fabrication and characterization method for a CIGS test pattern, and the other one is the fabrication and characterization method of a real CIGS solar cell.2.12.1.1iLP values along the CIGS thin film (ro) of 1 mm, and CIGS/Mo is planar stacked. Mo was prepared by DC-sputtering on a soda-lime glass, CIGS layer by three stage co-evaporation, CdS by chemical bath deposition, and Al/Ni by e-beam evaporation. More details on the preparation methods can be found elsewhere [iLP values.A CIGS test pattern with a structure of Al/Ni/CdS/CIGS/Mo was custom-designed to measure the hin film . Al/Ni/Clsewhere ,4. Mo, C2.1.2iLP values in the CIGS test pattern (iLP values were measured from a short-circuit condition between Al and Mo (Here, we describe the measurement of components which are necessary to measure the wavelength-dependent  pattern . A whitel and Mo .2.2A CIGS solar cell has a planar structure of ZnO:Al/i-ZnO/CdS/CIGS/Mo (


The 3-h high-sensitivity cardiac troponin T (hs-cTnT) algorithm is the most commonly used scheme to diagnose acute myocardial infarction. The 1-h hs-cTnT algorithm has recently been approved by the European Society of Cardiology as an alternative algorithm for earlier diagnosis. If the hs-cTnT test cannot discriminate the diagnosis of the patient at 1 h, the patient is defined as observational group. Their test must be repeated at 3 h. A high prevalence of this group may indicate a low clinical utility of the 1-h hs-cTnT algorithm. This study was aimed to estimate the proportion of the observational group in Thai emergency department (ED) patients and also the time to rule-in/out between both the algorithms.A historical control study was conducted in patients with chest pain for 1–12 h at the ED of Siriraj Hospital, Bangkok, Thailand. The study compared two groups: one prospective group of all patients evaluated with the 1-h hs-cTnT algorithm between June and September 2017 and one historical control group evaluated with the 3-h hs-cTnT algorithm between January and March 2017.n = 65 and 1-h hs-cTnT algorithm group n = 65). Twelve patients [18.5% (95% CI 10.0–30.1)] were categorized as observational group in the 1-h hs-cTnT algorithm group. The mean rule-in/out times in the 3-h hs-cTnT algorithm and 1-h hs-cTnT algorithm groups were 238 min (SD 63.3) and 134 min (SD 68.5), respectively (both p < 0.001). The time to disposition was also shortened in the 1-h hs-cTnT algorithm group (p <  0.001). Multivariable regression analysis performed to identify and adjust for confounders among patient characteristics revealed no significant confounders.A total of 130 patients were included (3-h hs-cTnT algorithm group The use of the 1-h hs-cTnT algorithm in the ED resulted in an acceptable proportion in the observational group and a decreased time to rule-in/out compared with the 3-h hs-cTnT algorithm. Acute myocardial infarction (AMI) is one of the major causes of mortality , 2. CardEarlier diagnosis and treatment of AMI can improve clinical outcome. Rapid exclusion is also helpful for the emergency department (ED) with limited resources or overcrowding. Recent approaches towards earlier diagnosis and exclusion using cutoff values below the 99th percentile have been proposed. The 1-h algorithm using hs-cTnT and hs-cTnI were developed and validated –17. The The 1-h hs-cTnT algorithm classifies patients into three groups: rule-in, rule-out, and observational group , nighttime of working day , and weekends or national public holidays.The study was conducted at the ED of Siriraj Hospital, Mahidol University, Bangkok, Thailand. The hospital is the largest tertiary and university hospital in Thailand with over 20,000 ED visits per year. The study was approved by the Siriraj Institutional Review Board . Informed consent was waived because the 1-h hs-cTnT algorithm was already validated, and the study did not affect rights, diagnostic adjustment, or clinical intervention of the patients.Participants were included if they were over 18 years of age and presented to the ED with chest pain or other symptoms suggestive of AMI with the onset in a duration of 1–12 h prior to presentation. Participants were excluded if they had ST elevation on electrocardiogram (ECG), had undergone defibrillation or cardioversion in their visit to the ED, had undergone coronary artery bypass grafting within the last month, and had been diagnosed as AMI within the last 3 weeks. Patients were also excluded if they had stage V chronic kidney disease, had end-stage renal disease, were pregnant, or were breastfeeding.All patients meeting the inclusion criteria were recruited prospectively and consecutively between 22 June and 12 September 2017. Investigations and treatments were given according to standard clinical practice guidelines. The 1-h hs-cTnT algorithm was conducted as shown in Fig. For this historical control group, medical records of patients presenting to the ED with chest pain or other symptoms suggestive of AMI with NSTE-ACS as the provisional diagnosis or a differential diagnosis from 8 January to 30 March 2017 were reviewed consecutively. We started with records on March 2017 and continued backwards in time until the quota of the comparable number of patients in each time of arrival interval was reached. In this group, the 3-h hs-cTnT algorithm was used to classify the patients. Investigations and management were also given according to standard clinical practice guidelines.We collected the baseline patient demographics, the time of ED arrival, the time of first blood drawing, the troponin test results, the time of receiving the specimen at the laboratory, and the time of reporting results. The time of patient discharged from the ED, the patient disposition at discharge, and the medications for acute coronary syndrome (ACS) received by the patient were also collected. Major adverse cardiac events (MACE), defined as composite events of all-cause mortality, AMI, percutaneous or surgical revascularization, and significant stenosis on coronary angiography that occurred within 30 days of ED arrival, was also documented. Furthermore, the number of patients in the ED at the time that the patients arrived was recorded. This information was for the evaluation of the effect of ED crowding.At Siriraj Hospital, the blood specimens were sent to the central laboratory unit (ISO 15189 approved laboratory) using messengers. Samples in both groups were assayed using Elecsys 2010 solution in Cobas 8000 machine .According to either the 1- or 3-h hs-cTnT algorithm, the patients’ category  after laboratory interpretation was recorded. In the observational group, the final category using the 3-h hs-cTnT result was recorded.This was the primary aim. The proportion was deemed acceptable if it was not more than that of the previous trials (22–24%) –17.This was the secondary aim. We calculated time intervals. The rule-in/out time was defined as the time of first blood drawing to the time of the last laboratory result reporting. Laboratory transport time was defined as the time taken to bring the specimen to the laboratory. Laboratory processing time was defined as the time the laboratory took to analyze and interpret the results.For the primary aim, a previous pilot study in Siriraj Hospital investigated 10 patients using the 1-h hs-cTnT algorithm, and the proportion of the observational group was 0.4 (40%). With a proportion of 0.4 (95% CI 0.28–0.52), a sample of 65 was required. For the secondary aim, a pilot study investigated 10 patients using the 3-h hs-cTnT algorithm (group 1) and 10 patients using the 1-h hs-cTnT algorithm (group 2). The mean difference in time to rule-in/out was about 80 min with an SD in group 1 of 65 min and in group 2 of 30 min. A difference in mean time of 60 min was considered clinically important by the consensus of the authors. Using a two-sided type I error of 0.01 and 90% power, we calculated a sample of 25 per group. To answer primary and secondary aims, a sample size of 65 patients per group was required. nQuery Advisor  was used to calculate the sample size.t tests as appropriate. Data with parametric distribution were reported as mean (SD). Data with nonparametric distribution were reported as median (IQR) and were compared by Mann-Whitney U test. Frequency was reported as number (%).Data were reported using descriptive statistics and compared by chi-square and two-sample t test was used to compare time to rule-in/out of AMI and other time intervals between the two groups. One-way ANOVA analysis was used to measure differences between subgroups. Multivariable linear regression was used to analyze factors influencing the secondary outcomes. Multivariable logistic regression was also used for triage type. Predictors were chosen by forward stepwise and backward stepwise selection. The variables examined included age, gender, onset of chest pain, current use of beta-blocker, nitrate, diuretic, ED prescription of aspirin, anticoagulant, nitroglycerin, intravenous furosemide, and oxygen treatment. All analyses used SPSS version 18 .The proportion of observational group in the post-implementation group was reported with 95% CI. A two-sample A total of 130 patients were recruited. From June to September 2017, 65 patients were prospectively enrolled in the post-implementation group. In the pre-implementation group, 65 patients matched on the time of the day and day of the week during which they visited to the ED with the post-implementation group were enrolled during January and March 2017 . They also had been taking more beta-blockers and diuretics prior to ED visit and had been given more treatments associated with ACS in the ED Table .Table 1Pp < 0.001) (Table There were 12 patients [18.5% (95% CI 10.0–30.1)] categorized as the observational group in the post-implementation group. The mean rule-in/out time was 238 min (SD 63.3) in the pre-implementation group and 134.3 min (SD 68.5) in the post-implementation group   Table . After ap = 0.25). Multivariable logistic regression model did not find any significant confounders among patient characteristics that predicted triage type. Disposition types were also not significantly different between the two groups . Of these, two patients were eventually ruled in according to the 3-h hs-cTnT algorithm. Both of them had further had cardiac testing. Of the 10 patients in the observational group who were eventually ruled out, two patients also had further cardiac testing. Of the observational group, two patients (16.7%) had 30-day MACE . However, no significant differences in the mean rule-in/out time, laboratory processing time, laboratory transport time, and time to disposition by time of day and day of week were found in subgroup analysis (Table The mean number of patients in the ED was significantly higher on daytime of working day [11.0 SD 3.2)] compared with nighttime of working day [8.7 (SD2.2)] and public holiday [8.6 (SD 2.9)] (.2] compaThe 1-h hs-cTnT algorithm is a validated alternative algorithm to the 3-h hs-cTnT algorithm for diagnosing AMI with the benefit of both earlier management for the rule-in group and earlier discharge for the rule-out group . HoweverThe proportion of the observational group in our sample was 18.5%. The proportion was even less than in reports of previous trials, which were between 22 and 24% –17. ThisWe found that the number of patients visiting the ED was highest during daytime of working day. Although we hypothesized that ED crowding might cause a delay in the diagnostic process, the subgroup analysis with regard to period of ED visit showed no significant differences. A trend towards longer duration on daytime of working day was found in all intervals although these were not significant. This might have been because of small numbers of patients in subgroup analyses.The laboratory processing time was longer in the pre-implementation group. This may have been caused by recent improvements in our laboratory analyzing system and process that affected the post-implementation group. However, in the post-implementation group, we found that the first laboratory process took about 13 min longer than the second. This might have been because the first laboratory examination usually consisted of not only cardiac troponin, but also other blood chemistry while the second laboratory taken 1 or 3 h later was usually troponin alone. Thus, the delay in the first laboratory process could have been from waiting for other blood chemistry results. If hs-cTnT had been analyzed and reported separately, the first laboratory processing time may have been similar to the second.Patients in the pre-implementation group appeared to be more at risk of having an AMI. The differences may have been due to enrollment of controls who were matched using retrospective data collection. It might also have caused a time-related benefit for the pre-implementation group. Because of the prolonged time from the onset of chest pain to presentation, patients in this group had a higher tendency towards having highly elevated first hs-cTnT results, which would have made the diagnosis possible earlier than in the post-implementation group. Nevertheless, the outcomes after controlling for these patient characteristic differences were not significantly different from the original values. Disposition type was also not significantly different between the two groups despite the higher admission rate in the pre-implementation group. The reasons for this high admission rate were the higher number of rule-in patients predisposed to being admitted and the higher number of rule-out patients admitted due to diagnoses other than AMI in the pre-implementation group.The 1-h hs-cTnT algorithm showed good discrimination to rule-in and rule-out patients suspected of AMI. The 1-h hs-cTnT algorithm had higher sensitivity, specificity, PPV, and NPV for 30-day MACE than the 3-h hs-cTnT algorithm. Nevertheless, this interpretation should be treated with caution given that each algorithm was performed on a different group of patients precluding direct comparison. Furthermore, the proportion of as much as 60% was seen in the rule-out category of the post-implementation group. The proportion that could be rapidly triaged as rule-in or rule-out at 1 h was as much as 81.5%. This percentage was comparable to the previous trials that externally validated this algorithm –17. NeveThere were many limitations in this study. First, the study design was a historical control design, which resulted in unequal patient characteristics. Although we controlled these in multivariable analysis, further studies properly matching by risk stratification should still be performed. Secondly, the sample size may have been too small to determine the effect of ED crowding. A further study with a large sample size may find a significant difference. Finally, this study was a single-centered study in a major university hospital tertiary care center. Thus, our findings may not be generalizable to other EDs in different Thai settings.The 1-h hs-cTnT algorithm was feasible because of the acceptable proportion in the observation group. It was beneficial for Thai ED patients with chest pain because it decreased the rule-in/out time compared with the 3-h hs-cTnT algorithm.


In 2015, the European Society of Cardiology introduced new guidelines for the diagnosis of acute coronary syndromes in patients presenting without persistent ST-segment elevation. These guidelines included the use of high-sensitivity troponin assays for ‘rule-in’ and ‘rule-out’ of acute myocardial injury at presentation (using a ‘0 hour’ blood test). Whilst these algorithms have been extensively validated in prospective diagnostic studies, the outcome of their implementation in routine clinical practice has not been described. The present study describes the change in the patient journey resulting from implementation of such an algorithm in a busy innercity Emergency Department.Data were prospectively collected from electronic records at a large Central London hospital over seven months spanning the periods before, during and after the introduction of a new high-sensitivity troponin rapid diagnostic algorithm modelled on the European Society of Cardiology guideline. Over 213 days, 4644 patients had high-sensitivity troponin T measured in the Emergency Department. Of these patients, 40.4% could be ‘ruled-out’ based on the high-sensitivity troponin T concentration at presentation, whilst 7.6% could be ‘ruled-in’. Adoption of the algorithm into clinical practice was associated with a 37.5% increase of repeat high-sensitivity troponin T measurements within 1.5 h for those patients classified as ‘intermediate risk’ on presentation.Introduction of a 0 hour ‘rule-in’ and ‘rule-out’ algorithm in routine clinical practice enables rapid triage of 48% of patients, and is associated with more rapid repeat testing in intermediate risk patients. The measurement of cardiac-specific biomarkers released into the circulation is invaluable, and the measurement of cardiac troponin (cTn) I and T is engrained in the universal definition of myocardial infarction.2 However, the slow release of cTn, in combination with the relative analytic insensitivity of conventional cTn assays, has necessitated serial measurements separated by at least six hours to increase both sensitivity and specificity. This period of diagnostic uncertainty prolongs the patient’s hospital stay, delays their treatment and has an associated fiscal cost. The advent of high sensitivity troponin assays has encouraged investigators to examine shorter intervals between repeat troponin estimations. The high sensitivity assays have also allowed the testing of diagnostic cut-off concentrations well below the population defined 99th centile to rapidly rule out acute myocardial injury. These innovations culminated in the European Society of Cardiology (ESC) releasing new guidelines in September 2015 for the management of patients without persistent ST elevation.36 These guidelines adopt a ‘rule-out’ troponin value significantly below the 99th centile and a ‘rule-in’ value well above the 99th centile. Between these values of diagnostic clarity, the change in troponin level over the course of one hour can guide further rule-in or rule-out. In October 2015 we proposed introduction of the 0 hour rule-in/rule-out component of the ESC algorithm at St Thomas’ Hospital  and adopted the guideline, following an internal consultation process, during December 2015–January 2016. This internal consultation process also involved extension of teaching to ED staff, both nursing and physician, as to the appropriate use of the algorithm. All ‘post-intervention’ data were collected after implementation and associated staff training.Chest pain and related complaints are estimated to account for 6% of all attendances to UK Emergency Departments (EDs).Whilst the ESC guidelines help to streamline the diagnostic pathway, there has been little information regarding their impact on front-line medical services. The present study, based in the ED of a large Central London hospital, aims to (a) prospectively assess the risk classification of patients based on 0 hour hs-cTnT measurement, and (b) examine the effect of clinical implementation of the 0 hour component of the ESC guideline on the patient pathway. In particular, we document changes in the pattern of repeat troponin measurements and overnight admission.th revision of the International Statistical Classification of Diseases and Related Health Problems (ICD-10) and were subsequently categorised into diagnostic groups by two adjudicators (JM and TEK).Data was prospectively collected on all high-sensitivity cardiac troponin T (hs-cTnT) assays performed on serum from patients presenting to the ED of St Thomas’ Hospital, between September 2015–March 2016. This time-period of data collection spans the pre-intervention (September-November), transition (December), and post-intervention (January-March) phases of algorithm implementation. hs-cTnT assays were performed using the Roche Elecsys platform  at 13 ng/l, limit of blank at 3 ng/l, limit of detection at 5 ng/l). The hs-cTnT value measured in the ED was matched to any subsequent hs-cTnT measurement on the same patient within 24 h. Further information on admission, admitting specialty, and length of stay was collected from electronic discharge records. Data on presenting symptom was obtained from the system used for triage and clinical tracking in the ED (Ascribe Symphony); this captures the prime medical complaint but, however, it does not encompass a physician’s interpretation. Discharge diagnoses are locally recorded according to the 10t-test), and for categorical variables as numbers and percentages (compared with Pearson chi-square). Hypothesis testing was two-tailed, and p values <0.05 were considered statistically significant. Statistical analysis was conducted using SPSS version 22  and R, version 3.3.0 GUI 1.68 , including ggplot2.The new algorithm for the diagnostic management of possible Non-ST elevation Acute Coronary Syndrome (NSTE-ACS) can be summarised as follows: hs-cTnT is measured on arrival to ED for patients with a history suggestive of Acute Coronary Syndrome (ACS), and an electrocardiogram (ECG) without persistent ST elevation. ACS can be ‘ruled-out’ in low-risk patients with a hs-cTnT on presentation of <5 ng/l, and ‘ruled-in’ for those patients with an initial hs-cTnT of >50 ng/l . Althougn=4644), chest pain was the primary presenting symptom in 45.7% (n=2120), and shortness of breath in 8.2% (n=382) – see Over a period of 213 days, spanning the introduction of the new diagnostic protocol, a total of 4644 patients had a hs-cTnT measurement in the ED. A summary of the presenting complaint of all patients with hs-cTnT measurements in the study period (September 2015 –March 2016) is presented in n=2120), 1026 (48.4%) had an initial hs-cTnT concentration below the ‘rule-out’ threshold, 107 (5%) had a concentration above the ‘rule-in’ threshold. Of the patients presenting with shortness of breath (n=382), 89 (23.3%) had an initial hs-cTnT concentration below the ‘rule-out’ threshold, 74 (19.4%) had a concentration above the ‘rule-in’ threshold.Of the entire cohort, 40.4% had an initial hs-cTnT concentration below the ‘rule-out’ value of 5 ng/l at presentation, and 7.6% had a concentration above the ‘rule-in’ value of 50 ng/l . Of the Although our algorithm incorporates only the rule-in/rule-out classification based on a 0 hour hs-cTnT measurement, retrospective analysis of the entire cohort demonstrates that 10.6% of those at intermediate risk (0 hour hs-cTnT 5–50 ng/l) could have been ruled-in on repeat testing with a ΔTnT ≥5 ng/l, and 45.1% could have been ruled-out on the basis of an initial TnT<12ng/l and ΔTnT<3 ng/l.n=397); congestive cardiac failure was the discharge diagnosis in 5.8%; pulmonary embolism in 1.5%. Of those patients admitted with a troponin value above the rule-in threshold (50 ng/l), 35.6% were diagnosed with ischaemic cardiac pathology , 946 patients (50.2%) had an initial hs-cTnT in the 5-50 ng/l zone of diagnostic uncertainty – of these, 443 (46.8%) had a repeat measurement within 24 h. Of the patients undergoing further testing, 189 (42.7%) had a repeat measurement within 1.5 h. Median time to repeat hs-cTnT measurement was 1.6 h  for the entire post-intervention period.Eight hundred and ninety-two patients presented with chest pain in the post-intervention period. Of these, 390 patients (43.7%) were in the observational group, of which 222 (56.9%) had a repeat measurement within 24 h. Of the patients undergoing further testing, 106 (47.7%) had a repeat measurement within 1.5 h. The median time to repeat hs-cTnT measurement in the group presenting with chest pain was 1.5 h .One hundred and fifty-four patients presented with shortness of breath in the post-intervention period. Of these, 87 patients (56.5%) were in the observational group, of which 29 (33.3%) had a repeat hs-cTnT within 24 h. Of the patients undergoing further testing, 10 (34.5%) had a repeat measurement within 1.5 h. Median time to repeat in the group presenting with shortness of breath was 1.8 h .p<0.001). In tandem, the median time to repeat troponin has fallen from 7.8 h  to 1.7 h  (p<0.001). This has been accompanied by a non-significant trend towards reduced overnight admissions in the low-risk group. In a month prior to implementation, of all patients with a hs-cTnT measurement <5 ng/l on presentation to ED, 12.7% were admitted for at least one night. This figure fell to 9.5% by month 7 . Early outcome data demonstrates that 30-day mortality in all patients with suspected ACS was not different before and after implementation of the new algorithm .Over the timeframe of implementation of the new algorithm we have demonstrated a gradual rise in the proportion of patients in the intermediate risk group  who had a repeat hs-cTnT measured within 1.5 h. At month 1 (pre-implementation), only 3.3% of repeat hs-cTnT measurements in the intermediate-risk patients were within 1.5 h, rising to 40.8% by month 7 (post-implementation) (This study documents the rate of adoption of a rapid rule-in/rule-out algorithm for the routine clinical care of patients presenting with suspected NSTE-ACS, based on a single blood test at presentation. In this large cohort of over 4600 patients, 48% of all patients and 53% of patients with chest pain could be dichotomised into high- or low risk groups on the basis of a single hs-cTnT measured on presentation.48 The unifying aim is to rapidly identify patients with ACS, facilitating prompt therapeutic intervention for those who need it, and prompt discharge for those who do not. However, since the ESC guidelines have been established, there is a dearth of studies that have addressed the fundamental question – can such an algorithm be implemented into routine clinical practice? As we have incorporated our algorithm into clinical practice, we have seen an increased rate of repeat testing, and a trend to faster repeats, in patients classified into the intermediate risk group on presentation. Whilst this algorithm should lead to empowerment of clinicians to exclude NSTE-ACS and discharge during the early stages of presentation, we have yet to observe a significant reduction in overnight admissions in the low-risk group.Multiple studies have prospectively validated the sensitivity and specificity of diagnostic algorithms based on high-sensitivity troponin assays.Whilst there is a clear trend in uptake of the protocol following its implementation, it is evident that it is still not being used universally across the services. This may reflect hesitancy amongst clinicians to discharge patients soon after presentation, without a significant period of monitoring. It is of paramount importance to involve all staff in understanding the rationale for change, optimising operation procedures to ensure rapid turn-around times for sequential blood draws and to streamline a rapid assessment process; in order to reap the benefits of an earlier rule-out.This study looks predominantly at the rule-in/rule-out power of the ESC algorithm at 0 hour, based on a hs-cTnT measurement at presentation. Whilst we have been able to retrospectively quantify the risk classification of patients based on ΔTnT, the translation of ΔTnT values into prospective clinical practice needs further evaluation. Although the ESC recommends a 0 h hs-cTnT ≥52 ng/l as a rule-in threshold, our algorithm defines rule in as >50 ng/l for ease of clinical implementation.3 The 0–1 h ESC algorithm suggests progression to biomarker risk stratification in the patient with ‘suspected Non-ST elevation Myocardial Infarction’ and does not delineate that this suspicion must arise from the presence of typical chest pain. As such, presenting complaints like isolated shortness of breath and abdominal pain, that feature in 9 approximately 12% of initial troponin samples taken in the ED were haemolysed. These samples were excluded from analysis as they inevitably lead to deviation from the algorithm, and this study aimed to look at the routine functioning of the algorithm in clinical practice. However, it is important to acknowledge that, in the real-world setting, haemolysis is likely to affect the timings of samples. Finally, the troponin values available electronically to clinicians are rounded to the nearest integer, which may lead to some discrepancy between the true risk bracket that the patient belonged to and the risk bracket that they were ascribed to clinically in ED.Chest pain is clearly the typical presentation of NSTE-ACS. However, the ESC guideline appreciates that ACS can present atypically as ‘epigastric pain, indigestion-like symptoms and isolated dyspnea’.A 0 hour rule-in/rule-out algorithm, modelled on the 2015 ESC guideline, can be implemented with good uptake within the first few months of introduction. Although this has failed to demonstrate reduced overnight admission in the low-risk group, the algorithm clarifies the appropriate clinical pathway for up to 53% of chest pain patients at presentation. Further studies are needed to address the implications of one-hour repeat testing in routine clinical practice.